IRELESS communication over fading channels remains one of the fundamental challenges in modern networks. In particular, Rayleigh fading channels, which model rich-scattering non-line-of-sight environments, cause rapid and unpredictable fluctuations in signal strength that can significantly degrade throughput and reliability. To mitigate these effects, link adaptation techniques such as adaptive modulation and coding (AMC) and power control have been extensively studied as key enablers of efficient spectrum use [1], [2]. Early works on link adaptation for Rayleigh fading channels demonstrated how explicit channel estimation and threshold-based switching could improve throughput and maintain robustness under fading conditions [3]-[6]. Despite their success, these classical approaches rely on accurate channel estimation, fixed rules, and often compromise between average throughput and outage probability in a suboptimal manner [4]-[6]. Furthermore, as networks evolve toward 6G with denser topologies and stringent reliability demands, such schemes struggle to scale or adapt to system-level complexities [7], [8]. Recent works have explored deep reinforcement learning (DRL) and meta reinforcement learning (RL) for link adaptation and resource allocation, showing promising adaptability but still facing high sample complexity and training instability [9]-[12]. In this letter, we propose quantum-preconditioned policy gradient (QPPG), a natural actor-critic method for link adap-Oluwaseyi Giwa is with the African Institute for Mathematical Sciences, South Africa (e-mail: {oluwaseyi}@aims.ac.za). Muhammad Ahmed Mohsin is with Stanford University, Stanford, California, 94305, United States (e-mail: {muahmed}@stanford.edu).

Artificial intelligence (AI) has emerged as a powerful tool for addressing complex and dynamic tasks in communication systems, where traditional rule-based algorithms often struggle. However, most AI applications to networking tasks are designed and trained for specific, limited conditions, hindering the algorithms from learning and adapting to generic situations, such as those met across radio access networks (RAN). This paper proposes design principles for sustainable and scalable AI integration in communication systems, focusing on creating AI algorithms that can generalize across network environments, intents, and control tasks. This approach enables a limited number of AI-driven RAN functions to tackle larger problems, improve system performance, and simplify lifecycle management. To achieve sustainability and automation, we introduce a scalable learning architecture that supports all deployed AI applications in the system. This architecture separates centralized learning functionalities from distributed actuation and inference functions, enabling efficient data collection and management, computational and storage resources optimization, and cost reduction. We illustrate these concepts by designing a generalized link adaptation algorithm, demonstrating the benefits of our proposed approach.

The Ericsson Intelligent RAN Automation portfolio, shown in Figure 1, features end-to-end network automation that includes centralized and distributed SON solutions and new capabilities that support the transformation to a more open environment enabled for AI/ML, which empowers innovation and support for wide range of use cases, shorter time to market and is highly adaptable supporting existing and future networks. The objective of RAN automation is to boost RAN performance and operational efficiency by replacing the manual work of developing, installing, deploying, managing, optimizing and retiring of RAN functions with automated processes. The AI's role is to unlock more advanced network automation performance to make RAN network functions more autonomous and replace manual processes with intelligent tools that augment humans. Furthermore, it makes both AI/ML powered RAN network functions and tools more robust for deployment in different environments. Ericsson AI and automation foundations gives service providers the platforms, and evolved life cycle management of RAN SW and services to evolve networks efficiently to successfully meet ever-changing demands.